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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Supervised and Unsupervised Learning with Numerical Computation for the Wolfram Cellular Automata.

Entropy (Basel, Switzerland)·2025
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Updated: May 10, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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An Event-Link Network Model Based on Representation in P-Space.

Wenjun Zhang1, Xiangna Chen2,3, Weibing Deng3

  • 1School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei 230012, China.

Entropy (Basel, Switzerland)
|April 26, 2025
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Summary

This study introduces an event-link model to generate complex networks in P-space, overcoming previous challenges. The model accurately replicates real-world network properties like small-world behavior and scale-free structure.

Keywords:
complex networkevent–link modelgrowing mechanismrepresentation in P-spacetransport network

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Area of Science:

  • Complex network analysis
  • Network modeling
  • Data science

Background:

  • L-space and P-space are key representations for complex networks.
  • Generating networks in P-space is challenging for existing models.
  • Understanding P-space network mechanisms is crucial.

Purpose of the Study:

  • Empirically analyze node distribution and network properties in P-space.
  • Propose a novel event-link model for P-space network generation.
  • Investigate the operational mechanisms of real-world networks in P-space.

Main Methods:

  • Empirical analysis of node distribution in P-space.
  • Development of an event-link model using real transportation network data.
  • Simulation experiments to evaluate topological features and parameter effects.

Main Results:

  • The event-link model successfully generates networks in P-space.
  • Simulations align with theoretical analysis.
  • Generated networks exhibit small-world, scale-free properties, and high clustering.

Conclusions:

  • The event-link model effectively generates networks with stable structures resembling real-world data.
  • Adjustable parameters allow flexible control over network growth and evolution.
  • The model provides insights into P-space network mechanisms.